SEHS: Simultaneous energy harvesting and sensing using piezoelectric energy harvester

• المؤلفون الرئيسيون: MA, Dong, LAN, Guohan, XU, Weitao, HASSAN, Mahbub, HU, Wen
• التنسيق: text
• اللغة: English
• منشور في: Institutional Knowledge at Singapore Management University 2018
• الموضوعات: Piezoelectric Energy Harvesting; Simultaneous Energy Harvesting and Sensing; Context Detection from EnergyHarvesting; Gait Recognition; Artificial Intelligence and Robotics
• الوصول للمادة أونلاين: https://ink.library.smu.edu.sg/sis_research/7006; https://ink.library.smu.edu.sg/context/sis_research/article/8009/viewcontent/SEHS_CR.pdf
• المؤسسة: Singapore Management University

Piezoelectric energy harvesting (PEH), which converts ambient motion, stress, and vibrations into usable electricity, may help combat battery issues in a growing number of industrial and wearable Internet of things (IoTs). Recently, many works have convincingly demonstrated that PEH can also act as a self-powered sensor for detecting a wide range of machine and human contexts. These developments suggest that the same PEH hardware could be potentially used for simultaneous energy harvesting and sensing (SEHS), offering a new design space for low cost and low power IoT devices. Unfortunately, realization of SEHS is challenging as the energy harvesting process distorts the sensing signal. To achieve high quality sensing from PEH, the state-of-the-art uses separate PEHs for sensing and energy harvesting, which increases system complexity, form factor, and cost. In this paper, we propose a novel SEHS architecture, which combines energy harvesting and sensing in the same piece of PEH, and minimizes distortion in the sensing signal by applying a special filtering algorithm. We prototype the SEHS concept in the form factor of a shoe, and evaluate its energy harvesting as well as sensing performance with 20 subjects using gait recognition as a case study. We demonstrate that the SEHS prototype harvests up to 127% more energy and detects human gait with 8% higher accuracy while consuming 35% less power compared to the state-of-the-art.